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这篇文章附着一个计算AUC的Java工具包:http://mark.goadrich.com/programs/AUC/ 一般认为,对于一个诊断实验,AUC在0.5~0.7之间时,诊断价值较低;在0.7~0.9之间,诊断价值中等;在0.9以上时诊断价值较高。这是医学诊断上的经验了,对于其他领域的分类器如何,还需要在实践中摸索。
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顾名思义,AUC的值就是处于ROC curve下方的那部分面积的大小。通常,AUC的值介于0.5到1.0之间,较大的AUC代表了较好的Performance。 AUC计算工具: http://mark.goadrich.com/programs/AUC/ P/R/F/map和ROC是两种不同的评价指标和计算方式,一般情况下,检索用前者,分类、识别等用后者。 reference...
问Cross_val_score不使用roc_auc和多类ENscikit交叉验证功能的一个不必要的麻烦是，默认情况下，数据不...
http://mark.goadrich.com/programs/AUC/ P/R和ROC是两个不同的评价指标和计算方式,一般情况下,检索用前者,分类、识别等用后者。 参考链接: http://www.vanjor.org/blog/2010/11/recall-precision/ http://bubblexc.com/y2011/148/ http://wenku.baidu.com/view/ef91f011cc7931b765ce15ec.html ...